Bibliographic record
Abstract
This paper encourages environmental and humane education scholars to consider the ethical implications of how nonhuman animals are represented in research. I argue that research representations of animals can work to either break down processes of “othering,” or reinforce them. I explore various options for representing other animals, including concrete examples demonstrating some researchers’ methodological and representation choices (including my own). Finally, I consider questions pertaining to evaluating the quality and effectiveness of alternative and less common forms of representation. Resume Le present article encourage les universitaires œuvrant en education environnementale et humaine a se pencher sur les implications ethiques des differentes facons de representer les animaux non humains en recherche. J’avance que les representations des animaux en recherche peuvent soit diminuer les processus d’« alienation », soit les renforcer. J’examine diverses options de representation des animaux, donnant des exemples concrets illustrant les choix methodologiques et representationnels de chercheurs (y compris les miens). Enfin, je me penche sur des questions relatives a l’evaluation de la qualite et de l’efficacite d’autres formes de representation moins courantes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".